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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Rajdeep Kumar Dutta Bishal Chhetry Rakesh Matam Ferdous Ahmed Barbhuiya Dept. of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model’s ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats.

关键词： Training data privacy Federated learning Scalability Reconnaissance data models Telecommunications Internet of Things Servers Faces

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Water Region Extraction in Aerial Images Using Deep Learning

Water Region Extraction in Aerial Images Using Deep Learning

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Nikita Pandey Saumya Kumar Satish Kumar Yadav Shashwat Srivastava Shomil Singh Deepak Gupta Dept. of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Water bodies are essential component of Earth’s surface and accurate water region mapping is crucial for disaster management and environmental monitoring. Existing methods to map water bodies relies on traditional modelling methods that need extensive manual calibration struggle with scalability and lack advanced pattern recognition. This paper evaluates the effectiveness of deep learning-based semantic segmentation models, DeepLabV3+ and U-Net, using aerial images resized to $\mathbf{2 5 6 x} 256$ pixels in mapping water regions in aerial images. DeepLabV3+, with a ResNet50 backbone, and U-Net are trained on a comprehensive flood-affected dataset with preprocessing techniques such as resizing and augmentation. Model performance is assessed using metrics like IoU, F1 score, accuracy, precision, and recall. The results indicate that DeepLabV3+ outperforms U-Net, showing promise for automating water region mapping and improving flood surveys and urban planning.

关键词： Training Measurement Deep learning Surveys Technological innovation Accuracy Computational modeling Semantic segmentation Urban planning Water resources

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Real-Time Network Monitoring: A Big data Approach

Real-Time Network Monitoring: A Big Data Approach

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International Conference on Computing and Networking Technology (ICCNT)

作者： Kunisetty Jaswanth S Sruthi Pranav Ramachandrula S. Saravanan Dept. of Computer Science Engineering Amrita School of Computing Bengaluru India Dept. of Computer Science Engineering Amrita School of Computing Chennai India

Real-time network monitoring is a critical requirement for tracking user activities and ensuring optimal network performance. In this paper, we propose a big data approach to real-time network monitoring that leverages the capabilities of Apache Spark, MongoDB, and Apache Kafka with Python. Our approach utilizes distributed stream processing of network data, enabling real-time analytics for user activity monitoring and network performance optimization. Our approach demonstrates the effectiveness of our system through experiments with real network traffic data, showing how it can identify and track user activities in real-time. Our system can be used by network administrators and IT professionals to optimize network performance, improve user experience, and enhance security through real-time monitoring and analysis of user activity data. This approach can also be opted as a feature in SDNs.

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A Wireless Intrusion Detection System using Feature Selection with Random Forest

A Wireless Intrusion Detection System using Feature Selectio...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Abhijit Sharad Warhekar Deepak D. Kshirsagar Dept. of Computer Science and Engineering COEP Technological University Pune Maharashtra India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Wireless networks have become integral to modern communication infrastructure, facilitating seamless connectivity in various domains. However, their presence everywhere also makes them vulnerable to various attacks. Those can cause failure in network services. Indeed, a robust Wireless Intrusion Detection System (WIDS) is essential for identifying and mitigating attacks on wireless networks. The key objective of WIDS is to detect wireless traffic and classify it as an attack or normal. In this situation, machine learning (ML) algorithms are used to detect attacks or intrusions in wireless systems. However, these algorithms need to accurately detect attacks early. Feature selection (FS) techniques are used to reduce this detection time and increase accuracy. These allow our system to identify attacks against local nodes. The proposed system is implemented, experimented and tested on AWID3 dataset. The performance of the system is determined by using a random forest (RF) algorithm and FS selection techniques. The experimentation conducted on AWID3 shows that the proposed methodology achieved an accuracy of 99.9990% for the binary model using 143 significant features and an accuracy of 99.9985% for multilabel classification using 144 significant features

关键词： Radio frequency Accuracy Machine learning algorithms Wireless networks Intrusion detection Telecommunication traffic Feature extraction Communication system security Security Random forests

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Optimization of ARIA Block Cipher Implementation on RISC-V Systems Through Instruction Extension

Optimization of ARIA Block Cipher Implementation on RISC-V S...

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International Conference on Platform Technology and Service (PlatCon)

作者： Dongwook Yun Muhammad Iqbal Howon Kim Dept. of Computer Science&Engineering Pusan National University Busan Korea

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

Cryptographic modules in Internet of Things (IoT) devices have had limited use, primarily to meet key requirements such as efficiency and lightweight. However, as IoT devices fulfill more complex roles and handle sensitive information, the need for efficient and lightweight cryptographic modules is increasing. However, traditional software- or hardware-based implementations to provide cryptographic modules result in a tradeoff between efficiency and lightweight. The emergence of the RISC-V architecture presents a new strategy to unify these distinct implementation approaches. RISC-V provides a modular Instruction Set Architecture (ISA) and support for user-extended instructions to enable the configuration of optimal system architectures. Therefore, a new cryptographic algorithm implementation method that integrates existing implementation methods can be presented. This paper presents various strategies for implementing ARIA block cipher, a Korean standard cryptographic algorithm, on a lightweight RISC-V system.

关键词： Ciphers Instruction sets Systems architecture Internet of Things Standards Optimization

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Design and Optimization of High-Performance Health Monitoring Systems Using Advanced Sensor Networks

Design and Optimization of High-Performance Health Monitorin...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Heena Mehta Mukesh Singla Dept. of Computer Science and Engineering Baba Mastnath University Rohtak Haryana India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. Advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution.

关键词： Wireless sensor networks Accuracy Protocols System performance Medical services Network architecture Real-time systems Biosensors Monitoring Optimization

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Analysis of Deep Learning Approaches to Predict Cardiac Arrhythmia in ECG Signals

Analysis of Deep Learning Approaches to Predict Cardiac Arrh...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Subham Kumar Padhy Saurav Mahalik Himanshu Nayak Sabyasachi Patra Anjali Mohapatra Dept. of Computer Science and Engineering DST-FIST Bioinformatics Lab IIIT Bhubaneswar India Dept. of Computer Science and Engineering IIIT Bhubaneswar India

Abnormal heart rhythm or irregular heartbeat, often known as arrhythmia. It is a kind of cardiovascular illness that necessitates a precise and fast diagnosis. Because of its simplicity and non-invasive nature, an electrocardiogram (ECG) that detects the electric activity of the heart has been frequently used to identify cardiac disorders. Each heartbeat's electrical signal, the peak of action impulse waveforms produced by various specialised cardiac tissues, can be used to diagnose various heart defects. Deep learning has evolved as a significant technique in recent decades due to its ability to handle vast amounts of data. The use of hidden layers in the convolution layer have enhanced pattern recognition performance. Deep learning has aided in the automation of medical image analysis and can aid in detecting of any anomalies in the medical images. In this work, ECG-based automated irregular heartbeat prediction is conducted to determine to which arrhythmia class it belongs with greater accuracy and less data loss. This study is based on convolutional neural networks, which are used to evaluate ECG images. For the normal case and cases impacted by various arrhythmias and my-ocardial infarction, the signals correspond to electrocardiogram (ECG) forms of ***-CNN, ResNet34, ResNet50, vgg16, and vgg19 models are utilised to predict of cardiac arrhythmia. vgg16 performed the best and is chosen to be further tweaked to improve accuracy to 99.79 percent.

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DeepLeaf-Attentive: Integrating Attention Mechanisms for Crop Leaf Disease Identification

DeepLeaf-Attentive: Integrating Attention Mechanisms for Cro...

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International Conference on computer and Information Technology (ICCIT)

作者： Tanvir Ahmed Farzana Sharmin Mou Dept. of Computer Science Engineering Dhaka University of Engineering & Technology Gazipur Dhaka Bangladesh Dept. of Computer Science University of Birmingham Birmingham UK

Crop leaf diseases have a detrimental impact on agricultural production. Visual inspection by organic farmers for disease detection is time-consuming and ineffective. To address this, we propose DeepLeaf-Attentive, a novel deep learning model for classifying crop leaf diseases. It combines MobileNet architecture with additional layers and attention mechanism. It is trained on a dataset of 30,000 images including data augmentation representing 60 classes with 500 images per class. The model achieves outstanding performance, with 97.21% accuracy, 97.72% precision, 97.43% recall, 97.49% F1-score and 18.0 ms inference time. Comparative evaluations against state-of-the-art approaches demonstrate the superiority of DeepLeaf-Attentive in crop leaf disease classification. By providing a reliable and efficient tool, our model will empower farmers to accurately identify and categorize various crop leaf diseases, facilitating informed decision-making and enabling the implementation of targeted and timely treatment strategies. The adoption of DeepLeaf-Attentive can significantly improve disease management practices in agriculture, leading to enhanced crop yields, reduced economic losses, and sustainable farming practices. By revolutionizing crop leaf disease identification, our research contributes to the advancement of precision agriculture, benefiting farmers and supporting global food security.

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Automatic Detection of Hypernasality in Cleft Palate Speech Using Phase Feature

Automatic Detection of Hypernasality in Cleft Palate Speech ...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Kanuri Naga venkata sai Hashreta Duggaraju Sri Lalitha Maddula Lalitha Lahari Pittala Deepthi Sri Akhilesh Kumar Dubey Dept. of computer science and engineering Koneru Lakshmaiah Education Foundation Guntur India

The detection of hypernasality, a speech disorder common in children with cleft palate, typically involves analyzing vowels within speech stimuli. An essential acoustic cue for hypernasality detection is the presence of a nasal peak near the first formant $(F_{1})$ . Traditionally, a two-step process is employed, where vowel selection from stimuli is initially performed through manual annotation, followed by automatic feature extraction and classification. This work introduces a novel method for hypernasality detection, automating the entire process, including vowel region selection, feature extraction, and classification. The method leverages vowel onset points for the automatic identification of the vowel region. Subsequently, a modified group delay cepstral coefficient feature is extracted from this automatically selected vowel region. The feature is extracted from modified group delay spectrum, offering enhanced resolution to distinguish closely spaced nasal peaks and the $F_{1}$ , thus improving the capture of nasality evidence. The proposed method demonstrates promising results when applied to hypernasality detection using a support vector machine classifier with the accuracy of 83.50% for /a/ vowel and 93.21% for /i/ vowel.

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Remote Training in Task-Oriented Communication: Supervised or Self-Supervised with Fine-Tuning?

arXiv

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arXiv 2025年

作者： Li, Hongru Zhao, Hang He, Hengtao Song, Shenghui Zhang, Jun Letaief, Khaled B. Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Task-oriented communication focuses on extracting and transmitting only the information relevant to specific tasks, effectively minimizing communication overhead. Most existing methods prioritize reducing this overhead during inference, often assuming feasible local training or minimal training communication resources. However, in real-world wireless systems with dynamic connection topologies, training models locally for each new connection is impractical, and task-specific information is often unavailable before establishing connections. Therefore, minimizing training overhead and enabling label-free, task-agnostic pre-training before the connection establishment are essential for effective task-oriented communication. In this paper, we tackle these challenges by employing a mutual information maximization approach grounded in self-supervised learning and information-theoretic analysis. We propose an efficient strategy that pre-trains the transmitter in a task-agnostic and label-free manner, followed by joint fine-tuning of both the transmitter and receiver in a task-specific, label-aware manner. Simulation results show that our proposed method reduces training communication overhead to about half that of full-supervised methods using the SGD optimizer, demonstrating significant improvements in training efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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